Markov Random Walk vs. Higher-Order Factorization Machines: A Comparison of State-of-the-Art Recommender Algorithms

  • World-wide research on recommender systems has resulted in great, highly effective algorithms based on a large variety of different concepts. Two of these promising recommender approaches are the Markov Random Walk and (higher-order) Factorization Machines. Unfortunately, due to the substantial effort for optimizing hyperparameters, most articles that describe new recommender approaches do not compare the obtained results with other state-of-the-art approaches in the recommender domain. This paper demonstrates how different state-of-the-art recommender algorithms can be compared in a consistent manner. Furthermore, we investigate under which circumstances Factorization Machines should be preferred and in which situations Markov Random Walk is the most striking algorithm. In addition, we include the restart concept into a Markov Random Walk with an optimized walk length and show how the number of factors of each order in a higher-order Factorization Machine can be optimized.

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Metadaten
Author:Julian Knoll, David Köckritz, Rainer GroßORCiD
DOI:https://doi.org/10.1007/978-3-319-60447-3_7
ISBN:9783319604466
ISSN:1865-0929
Parent Title (English):Communications in Computer and Information Science
Publisher:Springer International Publishing
Place of publication:Cham
Document Type:Part of a Book
Language:English
Date of first Publication:2017/06/02
Reviewed:Begutachtet/Reviewed
Release Date:2025/01/27
Pagenumber:17
First Page:87
Last Page:103
institutes:Fakultät Informatik
Research Themes:Digitalisierung & Künstliche Intelligenz
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